Estimates of atmospheric conditions and surface temperatures of terrestrial objects are important both for practical applications associated with space exploration, space commercialization and military operations as well as theoretical uses, including education and research within the fields of astronomy, astrophysics, etc. Such estimates are typically derived in part from remote measures, or images, of radiance emitted from the object. Information derived from the images is subsequently manipulated vi mathematical modeling to correct any altering effect of the atmosphere, pollution, etc., on the images as they are transmitted from the object to the observer or sensor. Existing mathematical functions ar available to describe the relationship between the radiance emitted from an object and the radiance sensed from the object.
There exists a number of prior art methods utilizing imaging to estimate surface temperatures of distant bodies. Traditionally, the estimation of information about a distant body has been accomplished using methods which combine imaging of the object to be studied with atmospheric conditions existing at the time and place of the imaging. Typically, these prior art methods utilize methods of radiometric modeling such as low-resolution atmospheric transmission (Lowtran) moderate-resolution atmospheric transmission (Modtran) or high-resolution transmission (Hitran) to describe the relationship between the radiance sensed and the radiance emitted.
Unfortunately, these methods require that the atmospheric conditions measured at the time and location of imaging be known quantities. Moreover, the use of such atmospheric condition information often introduces margins of error to the estimations due to the fact that the atmospheric conditions are estimated from values measured at remote distances and times from the time and place of imaging.
Other prior art methods employ specialized transmission models such as the Sea Surface Temperature (SST) Algorithm. As the name suggest, however, this method is only applicable to particular cases where the surface to be studied is imaged through maritime atmospheric conditions. Additionally, this method suffers from the same known atmospheric condition quantity limitations associated with the methods discussed above.
Thus a need has arisen for a method of estimating both atmospheric conditions and surface temperatures of terrestrial bodies from either a single set of multispectral images or multiple simultaneously-acquired single-band images of the body independent of the availability of atmospheric information at the time and location of the imaging.